Overview

Dataset statistics

Number of variables11
Number of observations6840
Missing cells690
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory641.2 KiB
Average record size in memory96.0 B

Variable types

Categorical2
Numeric9

Alerts

Country has a high cardinality: 228 distinct valuesHigh cardinality
code has a high cardinality: 205 distinct valuesHigh cardinality
Schizophrenia is highly overall correlated with Eating_disorder and 2 other fieldsHigh correlation
Bipolar_disorder is highly overall correlated with Eating_disorder and 2 other fieldsHigh correlation
Eating_disorder is highly overall correlated with Schizophrenia and 4 other fieldsHigh correlation
Anxiety is highly overall correlated with Bipolar_disorder and 2 other fieldsHigh correlation
drug_usage is highly overall correlated with Schizophrenia and 3 other fieldsHigh correlation
alcohol is highly overall correlated with drug_usageHigh correlation
mental_disorder is highly overall correlated with Schizophrenia and 4 other fieldsHigh correlation
code has 690 (10.1%) missing valuesMissing
Country is uniformly distributedUniform
code is uniformly distributedUniform
Bipolar_disorder has unique valuesUnique
Eating_disorder has unique valuesUnique
Anxiety has unique valuesUnique
drug_usage has unique valuesUnique
depression has unique valuesUnique
alcohol has unique valuesUnique
mental_disorder has unique valuesUnique

Reproduction

Analysis started2023-07-17 02:04:57.599892
Analysis finished2023-07-17 02:05:11.472635
Duration13.87 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Country
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct228
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size106.9 KiB
Afghanistan
 
30
Northern Ireland
 
30
Norway
 
30
OECD Countries
 
30
Oman
 
30
Other values (223)
6690 

Length

Max length34
Median length30
Mean length9.8421053
Min length3

Characters and Unicode

Total characters67320
Distinct characters59
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan

Common Values

ValueCountFrequency (%)
Afghanistan 30
 
0.4%
Northern Ireland 30
 
0.4%
Norway 30
 
0.4%
OECD Countries 30
 
0.4%
Oman 30
 
0.4%
Pakistan 30
 
0.4%
Palau 30
 
0.4%
Palestine 30
 
0.4%
Panama 30
 
0.4%
Papua New Guinea 30
 
0.4%
Other values (218) 6540
95.6%

Length

2023-07-17T07:35:11.615044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wb 210
 
2.1%
who 180
 
1.8%
and 180
 
1.8%
region 180
 
1.8%
islands 150
 
1.5%
world 150
 
1.5%
bank 120
 
1.2%
asia 120
 
1.2%
120
 
1.2%
income 120
 
1.2%
Other values (255) 8670
85.0%

Most occurring characters

ValueCountFrequency (%)
a 9120
 
13.5%
i 5430
 
8.1%
n 5160
 
7.7%
e 4530
 
6.7%
r 3870
 
5.7%
o 3720
 
5.5%
3360
 
5.0%
t 2520
 
3.7%
u 2280
 
3.4%
l 2220
 
3.3%
Other values (49) 25110
37.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 52320
77.7%
Uppercase Letter 10500
 
15.6%
Space Separator 3360
 
5.0%
Close Punctuation 420
 
0.6%
Open Punctuation 420
 
0.6%
Other Punctuation 150
 
0.2%
Dash Punctuation 90
 
0.1%
Decimal Number 60
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 9120
17.4%
i 5430
10.4%
n 5160
9.9%
e 4530
 
8.7%
r 3870
 
7.4%
o 3720
 
7.1%
t 2520
 
4.8%
u 2280
 
4.4%
l 2220
 
4.2%
d 2130
 
4.1%
Other values (16) 11340
21.7%
Uppercase Letter
ValueCountFrequency (%)
S 1080
 
10.3%
B 930
 
8.9%
A 780
 
7.4%
M 750
 
7.1%
C 690
 
6.6%
W 600
 
5.7%
I 570
 
5.4%
N 540
 
5.1%
E 510
 
4.9%
G 510
 
4.9%
Other values (15) 3540
33.7%
Other Punctuation
ValueCountFrequency (%)
& 120
80.0%
' 30
 
20.0%
Decimal Number
ValueCountFrequency (%)
2 30
50.0%
0 30
50.0%
Space Separator
ValueCountFrequency (%)
3360
100.0%
Close Punctuation
ValueCountFrequency (%)
) 420
100.0%
Open Punctuation
ValueCountFrequency (%)
( 420
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 90
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 62820
93.3%
Common 4500
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 9120
14.5%
i 5430
 
8.6%
n 5160
 
8.2%
e 4530
 
7.2%
r 3870
 
6.2%
o 3720
 
5.9%
t 2520
 
4.0%
u 2280
 
3.6%
l 2220
 
3.5%
d 2130
 
3.4%
Other values (41) 21840
34.8%
Common
ValueCountFrequency (%)
3360
74.7%
) 420
 
9.3%
( 420
 
9.3%
& 120
 
2.7%
- 90
 
2.0%
' 30
 
0.7%
2 30
 
0.7%
0 30
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 9120
 
13.5%
i 5430
 
8.1%
n 5160
 
7.7%
e 4530
 
6.7%
r 3870
 
5.7%
o 3720
 
5.5%
3360
 
5.0%
t 2520
 
3.7%
u 2280
 
3.4%
l 2220
 
3.3%
Other values (49) 25110
37.3%

code
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct205
Distinct (%)3.3%
Missing690
Missing (%)10.1%
Memory size106.9 KiB
VCT
 
30
NIU
 
30
PRK
 
30
MKD
 
30
MNP
 
30
Other values (200)
6000 

Length

Max length8
Median length3
Mean length3.0243902
Min length3

Characters and Unicode

Total characters18600
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAFG
2nd rowAFG
3rd rowAFG
4th rowAFG
5th rowAFG

Common Values

ValueCountFrequency (%)
VCT 30
 
0.4%
NIU 30
 
0.4%
PRK 30
 
0.4%
MKD 30
 
0.4%
MNP 30
 
0.4%
NOR 30
 
0.4%
OMN 30
 
0.4%
PAK 30
 
0.4%
PLW 30
 
0.4%
PSE 30
 
0.4%
Other values (195) 5850
85.5%
(Missing) 690
 
10.1%

Length

2023-07-17T07:35:11.778596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vct 30
 
0.5%
bhs 30
 
0.5%
alb 30
 
0.5%
dza 30
 
0.5%
asm 30
 
0.5%
and 30
 
0.5%
ago 30
 
0.5%
atg 30
 
0.5%
arg 30
 
0.5%
arm 30
 
0.5%
Other values (195) 5850
95.1%

Most occurring characters

ValueCountFrequency (%)
R 1470
 
7.9%
N 1320
 
7.1%
A 1320
 
7.1%
M 1260
 
6.8%
S 1080
 
5.8%
L 1050
 
5.6%
B 960
 
5.2%
T 960
 
5.2%
G 900
 
4.8%
U 810
 
4.4%
Other values (17) 7470
40.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 18570
99.8%
Connector Punctuation 30
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 1470
 
7.9%
N 1320
 
7.1%
A 1320
 
7.1%
M 1260
 
6.8%
S 1080
 
5.8%
L 1050
 
5.7%
B 960
 
5.2%
T 960
 
5.2%
G 900
 
4.8%
U 810
 
4.4%
Other values (16) 7440
40.1%
Connector Punctuation
ValueCountFrequency (%)
_ 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18570
99.8%
Common 30
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 1470
 
7.9%
N 1320
 
7.1%
A 1320
 
7.1%
M 1260
 
6.8%
S 1080
 
5.8%
L 1050
 
5.7%
B 960
 
5.2%
T 960
 
5.2%
G 900
 
4.8%
U 810
 
4.4%
Other values (16) 7440
40.1%
Common
ValueCountFrequency (%)
_ 30
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 1470
 
7.9%
N 1320
 
7.1%
A 1320
 
7.1%
M 1260
 
6.8%
S 1080
 
5.8%
L 1050
 
5.6%
B 960
 
5.2%
T 960
 
5.2%
G 900
 
4.8%
U 810
 
4.4%
Other values (17) 7470
40.2%

Year
Real number (ℝ)

Distinct30
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2004.5
Minimum1990
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.9 KiB
2023-07-17T07:35:11.906683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1990
5-th percentile1991
Q11997
median2004.5
Q32012
95-th percentile2018
Maximum2019
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.6560742
Coefficient of variation (CV)0.0043183209
Kurtosis-1.2026715
Mean2004.5
Median Absolute Deviation (MAD)7.5
Skewness0
Sum13710780
Variance74.927621
MonotonicityNot monotonic
2023-07-17T07:35:12.064506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1990 228
 
3.3%
1991 228
 
3.3%
2018 228
 
3.3%
2017 228
 
3.3%
2016 228
 
3.3%
2015 228
 
3.3%
2014 228
 
3.3%
2013 228
 
3.3%
2012 228
 
3.3%
2011 228
 
3.3%
Other values (20) 4560
66.7%
ValueCountFrequency (%)
1990 228
3.3%
1991 228
3.3%
1992 228
3.3%
1993 228
3.3%
1994 228
3.3%
1995 228
3.3%
1996 228
3.3%
1997 228
3.3%
1998 228
3.3%
1999 228
3.3%
ValueCountFrequency (%)
2019 228
3.3%
2018 228
3.3%
2017 228
3.3%
2016 228
3.3%
2015 228
3.3%
2014 228
3.3%
2013 228
3.3%
2012 228
3.3%
2011 228
3.3%
2010 228
3.3%

Schizophrenia
Real number (ℝ)

Distinct6838
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28116734
Minimum0.19162102
Maximum0.50601816
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.9 KiB
2023-07-17T07:35:12.351461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.19162102
5-th percentile0.2080532
Q10.25546818
median0.28745649
Q30.30475992
95-th percentile0.34495671
Maximum0.50601816
Range0.31439714
Interquartile range (IQR)0.049291741

Descriptive statistics

Standard deviation0.047560805
Coefficient of variation (CV)0.1691548
Kurtosis2.4407976
Mean0.28116734
Median Absolute Deviation (MAD)0.021966536
Skewness0.70985116
Sum1923.1846
Variance0.0022620302
MonotonicityNot monotonic
2023-07-17T07:35:12.476775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.297248345 2
 
< 0.1%
0.214853086 2
 
< 0.1%
0.304513278 1
 
< 0.1%
0.275360986 1
 
< 0.1%
0.275538338 1
 
< 0.1%
0.275450423 1
 
< 0.1%
0.275226548 1
 
< 0.1%
0.274869425 1
 
< 0.1%
0.274384564 1
 
< 0.1%
0.273772828 1
 
< 0.1%
Other values (6828) 6828
99.8%
ValueCountFrequency (%)
0.19162102 1
< 0.1%
0.191623698 1
< 0.1%
0.191626406 1
< 0.1%
0.191645978 1
< 0.1%
0.191671582 1
< 0.1%
0.191701254 1
< 0.1%
0.191717465 1
< 0.1%
0.191773165 1
< 0.1%
0.191850327 1
< 0.1%
0.191892699 1
< 0.1%
ValueCountFrequency (%)
0.506018159 1
< 0.1%
0.505987244 1
< 0.1%
0.505683549 1
< 0.1%
0.505567542 1
< 0.1%
0.505018433 1
< 0.1%
0.504038791 1
< 0.1%
0.503822155 1
< 0.1%
0.501304204 1
< 0.1%
0.500509251 1
< 0.1%
0.496664313 1
< 0.1%

Bipolar_disorder
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct6840
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.67389078
Minimum0.1893437
Maximum1.6762043
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.9 KiB
2023-07-17T07:35:12.610198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.1893437
5-th percentile0.27841908
Q10.53979086
median0.59189321
Q30.89724786
95-th percentile1.0443839
Maximum1.6762043
Range1.4868606
Interquartile range (IQR)0.35745701

Descriptive statistics

Standard deviation0.25859406
Coefficient of variation (CV)0.3837329
Kurtosis-0.28122084
Mean0.67389078
Median Absolute Deviation (MAD)0.21879051
Skewness0.29676474
Sum4609.4129
Variance0.066870889
MonotonicityNot monotonic
2023-07-17T07:35:12.742918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.721206856 1
 
< 0.1%
0.267383614 1
 
< 0.1%
0.267089927 1
 
< 0.1%
0.267006162 1
 
< 0.1%
0.266963457 1
 
< 0.1%
0.266926397 1
 
< 0.1%
0.266904082 1
 
< 0.1%
0.266893428 1
 
< 0.1%
0.266893188 1
 
< 0.1%
0.902528484 1
 
< 0.1%
Other values (6830) 6830
99.9%
ValueCountFrequency (%)
0.189343704 1
< 0.1%
0.189414527 1
< 0.1%
0.189422865 1
< 0.1%
0.189493611 1
< 0.1%
0.189549282 1
< 0.1%
0.189588083 1
< 0.1%
0.189605987 1
< 0.1%
0.189639025 1
< 0.1%
0.189700157 1
< 0.1%
0.189712183 1
< 0.1%
ValueCountFrequency (%)
1.676204333 1
< 0.1%
1.6758283 1
< 0.1%
1.675613557 1
< 0.1%
1.674588739 1
< 0.1%
1.673943421 1
< 0.1%
1.67355677 1
< 0.1%
1.673051748 1
< 0.1%
1.672880833 1
< 0.1%
1.672525865 1
< 0.1%
1.67076418 1
< 0.1%

Eating_disorder
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct6840
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21106163
Minimum0.045425136
Maximum1.1365408
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.9 KiB
2023-07-17T07:35:12.905741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.045425136
5-th percentile0.07469938
Q10.099856656
median0.15414285
Q30.27689055
95-th percentile0.50817254
Maximum1.1365408
Range1.0911157
Interquartile range (IQR)0.1770339

Descriptive statistics

Standard deviation0.15255899
Coefficient of variation (CV)0.72281728
Kurtosis3.7550546
Mean0.21106163
Median Absolute Deviation (MAD)0.062638289
Skewness1.7123071
Sum1443.6615
Variance0.023274246
MonotonicityNot monotonic
2023-07-17T07:35:13.035382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.131001451 1
 
< 0.1%
0.076755618 1
 
< 0.1%
0.07689237 1
 
< 0.1%
0.076681777 1
 
< 0.1%
0.076452937 1
 
< 0.1%
0.076318486 1
 
< 0.1%
0.075728335 1
 
< 0.1%
0.075222654 1
 
< 0.1%
0.074457957 1
 
< 0.1%
0.300139527 1
 
< 0.1%
Other values (6830) 6830
99.9%
ValueCountFrequency (%)
0.045425136 1
< 0.1%
0.045498681 1
< 0.1%
0.04555535 1
< 0.1%
0.045562531 1
< 0.1%
0.045606404 1
< 0.1%
0.045615218 1
< 0.1%
0.045676037 1
< 0.1%
0.045741125 1
< 0.1%
0.045796428 1
< 0.1%
0.045821632 1
< 0.1%
ValueCountFrequency (%)
1.13654079 1
< 0.1%
1.120082101 1
< 0.1%
1.111760481 1
< 0.1%
1.109898149 1
< 0.1%
1.10839309 1
< 0.1%
1.105394906 1
< 0.1%
1.101518904 1
< 0.1%
1.098768074 1
< 0.1%
1.094535522 1
< 0.1%
1.089447518 1
< 0.1%

Anxiety
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct6840
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3275248
Minimum1.9748234
Maximum9.015948
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.9 KiB
2023-07-17T07:35:13.144764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.9748234
5-th percentile2.7856548
Q13.5670642
median4.0944433
Q34.7972856
95-th percentile6.842225
Maximum9.015948
Range7.0411247
Interquartile range (IQR)1.2302214

Descriptive statistics

Standard deviation1.1779612
Coefficient of variation (CV)0.27220206
Kurtosis1.5055103
Mean4.3275248
Median Absolute Deviation (MAD)0.58804192
Skewness1.0823863
Sum29600.27
Variance1.3875925
MonotonicityNot monotonic
2023-07-17T07:35:13.269834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.83512726 1
 
< 0.1%
3.988553289 1
 
< 0.1%
3.97959901 1
 
< 0.1%
3.97620389 1
 
< 0.1%
3.973003849 1
 
< 0.1%
3.968807428 1
 
< 0.1%
3.96403039 1
 
< 0.1%
3.958688577 1
 
< 0.1%
3.953423718 1
 
< 0.1%
3.884646208 1
 
< 0.1%
Other values (6830) 6830
99.9%
ValueCountFrequency (%)
1.97482337 1
< 0.1%
1.975118962 1
< 0.1%
1.976211275 1
< 0.1%
1.97771316 1
< 0.1%
1.979591946 1
< 0.1%
1.981502419 1
< 0.1%
1.983840582 1
< 0.1%
1.98735 1
< 0.1%
1.992133165 1
< 0.1%
1.998584389 1
< 0.1%
ValueCountFrequency (%)
9.015948033 1
< 0.1%
9.011537882 1
< 0.1%
8.993180683 1
< 0.1%
8.957022058 1
< 0.1%
8.921286742 1
< 0.1%
8.912767871 1
< 0.1%
8.899568877 1
< 0.1%
8.794517454 1
< 0.1%
8.791172581 1
< 0.1%
8.780291986 1
< 0.1%

drug_usage
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct6840
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.74670789
Minimum0.2254705
Maximum3.6995038
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.9 KiB
2023-07-17T07:35:13.395269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.2254705
5-th percentile0.30321993
Q10.42350185
median0.64604956
Q30.89001272
95-th percentile1.6956725
Maximum3.6995038
Range3.4740333
Interquartile range (IQR)0.46651087

Descriptive statistics

Standard deviation0.46302612
Coefficient of variation (CV)0.62009003
Kurtosis5.9398511
Mean0.74670789
Median Absolute Deviation (MAD)0.22852974
Skewness2.0788498
Sum5107.482
Variance0.21439319
MonotonicityNot monotonic
2023-07-17T07:35:13.504675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.454202284 1
 
< 0.1%
0.661928344 1
 
< 0.1%
0.658095752 1
 
< 0.1%
0.657088781 1
 
< 0.1%
0.656977631 1
 
< 0.1%
0.656804694 1
 
< 0.1%
0.656532118 1
 
< 0.1%
0.657223057 1
 
< 0.1%
0.657338476 1
 
< 0.1%
0.574341154 1
 
< 0.1%
Other values (6830) 6830
99.9%
ValueCountFrequency (%)
0.225470501 1
< 0.1%
0.225815447 1
< 0.1%
0.225901895 1
< 0.1%
0.227166229 1
< 0.1%
0.227292883 1
< 0.1%
0.227843945 1
< 0.1%
0.228917582 1
< 0.1%
0.230646149 1
< 0.1%
0.23322906 1
< 0.1%
0.235266194 1
< 0.1%
ValueCountFrequency (%)
3.699503796 1
< 0.1%
3.674890451 1
< 0.1%
3.63768148 1
< 0.1%
3.601911828 1
< 0.1%
3.583965529 1
< 0.1%
3.556867028 1
< 0.1%
3.552769028 1
< 0.1%
3.477397135 1
< 0.1%
3.456076121 1
< 0.1%
3.382384178 1
< 0.1%

depression
Real number (ℝ)

Distinct6840
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9504487
Minimum1.6409017
Maximum7.6882127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.9 KiB
2023-07-17T07:35:13.629683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.6409017
5-th percentile2.7122978
Q13.2589774
median3.9041169
Q34.5505046
95-th percentile5.5205914
Maximum7.6882127
Range6.047311
Interquartile range (IQR)1.2915272

Descriptive statistics

Standard deviation0.92102112
Coefficient of variation (CV)0.23314342
Kurtosis0.59385124
Mean3.9504487
Median Absolute Deviation (MAD)0.64560918
Skewness0.55704851
Sum27021.069
Variance0.8482799
MonotonicityNot monotonic
2023-07-17T07:35:13.739065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.125291308 1
 
< 0.1%
3.204374315 1
 
< 0.1%
3.216704974 1
 
< 0.1%
3.219831813 1
 
< 0.1%
3.220500556 1
 
< 0.1%
3.221733433 1
 
< 0.1%
3.223204271 1
 
< 0.1%
3.22318998 1
 
< 0.1%
3.221909043 1
 
< 0.1%
3.120051421 1
 
< 0.1%
Other values (6830) 6830
99.9%
ValueCountFrequency (%)
1.640901729 1
< 0.1%
1.643316907 1
< 0.1%
1.64475768 1
< 0.1%
1.650701701 1
< 0.1%
1.654644981 1
< 0.1%
1.657092984 1
< 0.1%
1.658331375 1
< 0.1%
1.658792058 1
< 0.1%
1.659790677 1
< 0.1%
1.660149307 1
< 0.1%
ValueCountFrequency (%)
7.68821275 1
< 0.1%
7.683887867 1
< 0.1%
7.668643628 1
< 0.1%
7.661370891 1
< 0.1%
7.648538117 1
< 0.1%
7.622935952 1
< 0.1%
7.597049319 1
< 0.1%
7.596065418 1
< 0.1%
7.541464815 1
< 0.1%
7.510545327 1
< 0.1%

alcohol
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct6840
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5788072
Minimum0.31990044
Maximum4.698694
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.9 KiB
2023-07-17T07:35:13.848447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.31990044
5-th percentile0.43586009
Q10.73282581
median1.4600449
Q32.2612621
95-th percentile3.2434462
Maximum4.698694
Range4.3787936
Interquartile range (IQR)1.5284362

Descriptive statistics

Standard deviation0.93465468
Coefficient of variation (CV)0.59200053
Kurtosis-0.11392384
Mean1.5788072
Median Absolute Deviation (MAD)0.76201831
Skewness0.67075305
Sum10799.041
Variance0.87357937
MonotonicityNot monotonic
2023-07-17T07:35:13.973455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.444036259 1
 
< 0.1%
0.773502056 1
 
< 0.1%
0.773801685 1
 
< 0.1%
0.774308834 1
 
< 0.1%
0.775087858 1
 
< 0.1%
0.776136266 1
 
< 0.1%
0.777315541 1
 
< 0.1%
0.778691094 1
 
< 0.1%
0.780021528 1
 
< 0.1%
1.65435756 1
 
< 0.1%
Other values (6830) 6830
99.9%
ValueCountFrequency (%)
0.319900437 1
< 0.1%
0.320038982 1
< 0.1%
0.320573802 1
< 0.1%
0.321313186 1
< 0.1%
0.321667588 1
< 0.1%
0.32362552 1
< 0.1%
0.324270677 1
< 0.1%
0.328263524 1
< 0.1%
0.332801548 1
< 0.1%
0.337383791 1
< 0.1%
ValueCountFrequency (%)
4.698694015 1
< 0.1%
4.616114141 1
< 0.1%
4.612252118 1
< 0.1%
4.606228044 1
< 0.1%
4.599298252 1
< 0.1%
4.595736547 1
< 0.1%
4.590083316 1
< 0.1%
4.584368745 1
< 0.1%
4.56985219 1
< 0.1%
4.565493558 1
< 0.1%

mental_disorder
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct6840
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8180618
Minimum0.21564704
Maximum13.761517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.9 KiB
2023-07-17T07:35:14.111106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.21564704
5-th percentile1.4242863
Q13.0065069
median4.6791771
Q36.3874877
95-th percentile8.7846474
Maximum13.761517
Range13.54587
Interquartile range (IQR)3.3809808

Descriptive statistics

Standard deviation2.2940293
Coefficient of variation (CV)0.47613115
Kurtosis-0.24642479
Mean4.8180618
Median Absolute Deviation (MAD)1.6858145
Skewness0.40347843
Sum32955.543
Variance5.2625704
MonotonicityNot monotonic
2023-07-17T07:35:14.303988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.696670483 1
 
< 0.1%
2.367970825 1
 
< 0.1%
2.481643798 1
 
< 0.1%
2.446697652 1
 
< 0.1%
2.419505634 1
 
< 0.1%
2.391383922 1
 
< 0.1%
2.366675859 1
 
< 0.1%
2.338679761 1
 
< 0.1%
2.363714347 1
 
< 0.1%
6.795390495 1
 
< 0.1%
Other values (6830) 6830
99.9%
ValueCountFrequency (%)
0.215647036 1
< 0.1%
0.711836238 1
< 0.1%
0.71237955 1
< 0.1%
0.7305128 1
< 0.1%
0.738998292 1
< 0.1%
0.754945144 1
< 0.1%
0.759866713 1
< 0.1%
0.797730373 1
< 0.1%
0.801467827 1
< 0.1%
0.817571315 1
< 0.1%
ValueCountFrequency (%)
13.76151657 1
< 0.1%
13.72057186 1
< 0.1%
13.69250903 1
< 0.1%
13.67020102 1
< 0.1%
13.60839063 1
< 0.1%
13.43594058 1
< 0.1%
13.36733778 1
< 0.1%
13.23790297 1
< 0.1%
13.02193767 1
< 0.1%
12.6947323 1
< 0.1%

Interactions

2023-07-17T07:35:09.701169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:34:58.711560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:00.186855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:01.653513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:03.103813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:04.668332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:06.169902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:07.506414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:08.715187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:09.825978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:34:58.887425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:00.356023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:01.820039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:03.447843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:04.838951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:06.323643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:07.632252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:08.831766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:09.945733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:34:59.066425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:00.527450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:01.981652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:03.603919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:04.999022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:06.485884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:07.750742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:08.948372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:10.068414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:34:59.249514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:00.688709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:02.136089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:03.764381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:05.153932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:06.652665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:07.867748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:09.060302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:10.193486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:34:59.405458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:00.853925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:02.311303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:03.920112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:05.323813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:06.803881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:07.985957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:09.164811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:10.314703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:34:59.580611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:01.020976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:02.469934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:04.082169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:05.484407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:06.969565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:08.271481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:09.282729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:10.435795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:34:59.738580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:01.186307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:02.631906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:04.237122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:05.648653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:07.118762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:08.386109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:09.389786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:10.600088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:34:59.887373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:01.337134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:02.799313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:04.381231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:05.803396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:07.252043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:08.485705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:09.502027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:10.747958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:00.037496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:01.485193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:02.936173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:04.520003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:05.971308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:07.384269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:08.602372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-17T07:35:09.598615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-07-17T07:35:14.413813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
YearSchizophreniaBipolar_disorderEating_disorderAnxietydrug_usagedepressionalcoholmental_disorder
Year1.0000.0720.0230.0970.0460.053-0.036-0.0110.190
Schizophrenia0.0721.0000.1430.5720.3030.696-0.4910.3530.633
Bipolar_disorder0.0230.1431.0000.7740.6300.3740.2580.3420.645
Eating_disorder0.0970.5720.7741.0000.6590.6520.0130.3800.846
Anxiety0.0460.3030.6300.6591.0000.4680.1240.1180.673
drug_usage0.0530.6960.3740.6520.4681.000-0.2680.5280.573
depression-0.036-0.4910.2580.0130.124-0.2681.000-0.057-0.065
alcohol-0.0110.3530.3420.3800.1180.528-0.0571.0000.217
mental_disorder0.1900.6330.6450.8460.6730.573-0.0650.2171.000

Missing values

2023-07-17T07:35:11.024961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-17T07:35:11.341549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CountrycodeYearSchizophreniaBipolar_disorderEating_disorderAnxietydrug_usagedepressionalcoholmental_disorder
0AfghanistanAFG19900.2289790.7212070.1310014.8351270.4542025.1252910.4440361.696670
1AfghanistanAFG19910.2281200.7199520.1263954.8217650.4471125.1163060.4442501.734281
2AfghanistanAFG19920.2273280.7184180.1218324.8014340.4411905.1065580.4455011.791189
3AfghanistanAFG19930.2264680.7174520.1179424.7893630.4355815.1003280.4459581.776779
4AfghanistanAFG19940.2255670.7170120.1145474.7849230.4318225.0994240.4457791.712986
5AfghanistanAFG19950.2247130.7166860.1111294.7808510.4285785.0984950.4454221.738272
6AfghanistanAFG19960.2236900.7163880.1077864.7772720.4263935.1005800.4448371.778098
7AfghanistanAFG19970.2224240.7161430.1039314.7752420.4237205.1054740.4439381.781815
8AfghanistanAFG19980.2211290.7161390.1003434.7773770.4224915.1137070.4426651.729402
9AfghanistanAFG19990.2200650.7163230.0979464.7820670.4212155.1204800.4414281.850988
CountrycodeYearSchizophreniaBipolar_disorderEating_disorderAnxietydrug_usagedepressionalcoholmental_disorder
6830ZimbabweZWE20100.2071800.5566400.0944903.2915690.6189483.4471271.7890211.606235
6831ZimbabweZWE20110.2073360.5571040.0948713.2929640.6138753.4574061.7793801.758867
6832ZimbabweZWE20120.2077410.5579080.0956393.2971240.6093783.4806001.7687821.905674
6833ZimbabweZWE20130.2082800.5588880.0969503.3028810.6038913.5085341.7577762.024167
6834ZimbabweZWE20140.2088570.5599290.0983563.3093900.6007243.5337371.7466752.112216
6835ZimbabweZWE20150.2093590.5608820.0996103.3157010.5996043.5486131.7349692.193166
6836ZimbabweZWE20160.2099790.5617680.1008213.3242300.6036583.5575081.6892812.279813
6837ZimbabweZWE20170.2106310.5626120.1016713.3305690.6080963.5641381.6518052.364265
6838ZimbabweZWE20180.2112370.5632830.1023983.3175000.6090653.5631411.6867112.472949
6839ZimbabweZWE20190.2119690.5638200.1029023.2839340.6106443.5545711.7767292.525892